Identification and functional analysis of anti-citrullinated protein antibodies in rheumatoid arthritis

Rheumatoid arthritis (RA) is a complex autoimmune disease and typically manifested by joint inflammation and bone erosion with approximately 0.5% of the global population affected. To date, it is believed that genetic predisposition (e.g. HLA-DRB1 alleles) and environment (e.g. cigarette smoking) are involved as risk factors for the development of RA. A hallmark of RA preceding the disease onset is the emergence of autoantibodies, including rheumatoid factors (RFs) and anticitrullinated protein antibodies (ACPAs). Being the most specific (>90%) and sensitive (>60%) autoantibodies in RA, ACPAs have been included in the clinical criteria for the classification of RA. The function of ACPAs in RA is still unclear. Although patients with ACPA positivity are associated with more severe arthritis and in vitro studies have shown certain pathogenic effects of ACPAs, the in vivo evidence remains lacking. On the other hand, extensive but common Nglycosylation in the variable domain of ACPAs (90%) has been unveiled, questioning if these Nglycans serve a functional role. In Study I, we expressed several monoclonal ACPAs derived from RA patients and identified their specificities using a panel of citrullinated peptides. We found one of the ACPAs, clone E4, could protect against collagen antibody induced arthritis in mice. The protection is joint-specific and depending on the interaction between E4 in complex with citrullinated alpha-enolase and FCGR2B on activated macrophages, enhancing the IL-10 secretion and supressing osteoclastogenesis by macrophages. In Study II, we focused on the variable domain glycans (VDGs) in ACPAs by employing crystallography, glycobiology and functional B cell assay. We showed that 1) VDGs are positioned in the vicinity of the paratope with an impact on the antigen-binding; 2) VDGs could enhance B cell activation, and 3) VDG-expressing B cell receptors stay longer on the cell surface. In Study III, we investigated the two most significant arthritis QTLs in inbred rats, Ncf1 and Clec4b, and showed that Ncf1 and Clec4b together modulate the severity of arthritis in rats and their expression on neutrophils modulate the production of reactive oxygen species by neutrophils. Taken together, the findings revealed a protective, rather than pathogenic effect of certain ACPAs in RA and elucidated the unique properties of VDGs in ACPAs and their functional impact on autoreactive B cells

Geometry-Aware Face Completion and Editing

Face completion is a challenging generation task because it requires generating visually pleasing new pixels that are semantically consistent with the unmasked face region. This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region. Firstly, a facial geometry estimator is learned to estimate facial landmark heatmaps and parsing maps from the unmasked face image. Then, an encoder-decoder structure generator serves to complete a face image and disentangle its mask areas conditioned on both the masked face image and the estimated facial geometry images. Besides, since low-rank property exists in manually labeled masks, a low-rank regularization term is imposed on the disentangled masks, enforcing our completion network to manage occlusion area with various shape and size. Furthermore, our network can generate diverse results from the same masked input by modifying estimated facial geometry, which provides a flexible mean to edit the completed face appearance. Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well

Attention-Set based Metric Learning for Video Face Recognition

Face recognition has made great progress with the development of deep learning. However, video face recognition (VFR) is still an ongoing task due to various illumination, low-resolution, pose variations and motion blur. Most existing CNN-based VFR methods only obtain a feature vector from a single image and simply aggregate the features in a video, which less consider the correlations of face images in one video. In this paper, we propose a novel Attention-Set based Metric Learning (ASML) method to measure the statistical characteristics of image sets. It is a promising and generalized extension of Maximum Mean Discrepancy with memory attention weighting. First, we define an effective distance metric on image sets, which explicitly minimizes the intra-set distance and maximizes the inter-set distance simultaneously. Second, inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to adapt set-aware global contents. Then ASML is naturally integrated into CNNs, resulting in an end-to-end learning scheme. Our method achieves state-of-the-art performance for the task of video face recognition on the three widely used benchmarks including YouTubeFace, YouTube Celebrities and Celebrity-1000.Comment: modify for ACP